Results 111 to 120 of about 492,986 (210)
MathematicsTo address the challenge of model selection in large-scale traffic flow prediction tasks, this paper proposes a dynamic multi-model selection framework based on Deep Contextual Bandits (DCB).
Silai Chen +6 more
doaj +1 more source
Deepening the real options debate: Real options as dynamic optimization
Strategic Management Journal, Volume 47, Issue 11, Page 2823-2870, November 2026.Abstract Research Summary Is real option theory useful for management research? This topic was hotly debated two decades ago. Real options were said to be inapplicable to management research and to lack conceptual distinctiveness. Whereas responses to the claim about the non‐distinctiveness of real options were disparate, the concern about the theory's
Arkadiy V. Sakhartov +2 more
wiley +1 more source
Sparse Nonparametric Contextual Bandits
CoRRWe study the benefits of sparsity in nonparametric contextual bandit problems, in which the set of candidate features is countably or uncountably infinite. Our contribution is two-fold. First, using a novel reduction to sequences of multi-armed bandit problems, we provide lower bounds on the minimax regret, which show that polynomial dependence on the ...
Hamish Flynn +2 more
openaire +3 more sources
Tree Ensembles for Contextual Bandits
Trans. Mach. Learn. Res.We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both standard and combinatorial settings. As part of this framework, we propose a novel method of estimating the uncertainty in tree ensemble predictions.
Hannes Nilsson +3 more
openaire +3 more sources
Linear Contextual Bandits with Knapsacks
, 2015 We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm.
Shipra Agrawal 0001, Nikhil R. Devanur
openaire +3 more sources
Selective Reviews of Bandit Problems in AI via a Statistical View
MathematicsReinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment.
Pengjie Zhou, Haoyu Wei, Huiming Zhang
doaj +1 more source
Distributionally Robust and Learning-Based Vehicle Routing for Disaster Blood Logistics
IEEE AccessIn humanitarian disasters, delivering blood and blood products rapidly and reliably is critical but logistically complex. This paper proposes a hybrid framework that integrates Distributionally Robust Optimization (DRO) for offline anticipative planning ...
I. Hssini
doaj +1 more source
MathematicsWe develop and evaluate a deep contextual bandit framework for multivariate off-policy evaluation within a controlled simulation-based validation setting.
Jong-Min Kim
doaj +1 more source
Strategic Linear Contextual Bandits
Advances in Neural Information Processing Systems 37To appear at NeurIPS ...
Thomas Kleine Buening +3 more
openaire +4 more sources
Integrating Contextual Causal Deep Networks and LLM-Guided Policies for Sequential Decision-Making
MathematicsSequential decision-making is critical for applications ranging from personalized recommendations to resource allocation. This study evaluates three decision policies—Greedy, Thompson Sampling (via Monte Carlo Dropout), and a zero-shot Large Language ...
Jong-Min Kim
doaj +1 more source